In natural language processing, word sense disambiguation (WSD) is the problem of determining which "sense" (meaning) of a word is activated by the use of the word in a particular context, a process that appears to be largely unconscious in people. Nowadays, Word Sense Disambiguation (WSD) is an important technique for many natural language processing applications such as information retrieval and machine translation. Among them, the WSD technique is used for machine translation to find the correct sense of a word in a specific context. In machine translation, the input sentences in the source language are disambiguated in order to translate correctly in the target language which is Myanmar language that has many ambiguous words. Therefore, Hidden Markov Model-based WSD method is used to resolve the ambiguity of words in Myanmar language. A Myanmar-English bilingual corpus is used as the training data. This system can solve the semantic ambiguous problems that usually happen in Myanmar-to-English translation.